PEACE：跨平台仇恨言论检测——一种因果引导框架
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
AI总结：
针对跨平台仇恨言论检测中源平台模型偏差、目标平台缺标注数据的问题，提出因果引导框架PEACE，利用文本情感与攻击性两类因果线索学习可泛化表征，经多平台实验验证其跨平台泛化有效性。
摘要：
仇恨言论检测是指检测旨在基于个人或群体的宗教、性别、性取向或其他特征诋毁他们的仇恨内容的任务。由于平台政策不同，不同群体表达仇恨的方式也存在差异。此外，部分平台缺乏标注数据，使得构建仇恨言论检测模型颇具挑战。为此，我们重新探讨能否为跨平台场景学习一个可泛化的仇恨言论检测模型，即利用一个（源）平台的数据训练模型，并将模型泛化到多个（目标）平台。现有泛化模型依赖语言线索或辅助信息，导致它们对源平台的特定标签或特定类型词汇（如辱骂性词汇）存在偏差，因而无法适用于目标平台。受社会心理学理论启发，我们致力于探究是否存在可被利用的内在因果线索，以学习能在这些分布偏移下检测仇恨言论的可泛化表征。为此，我们提出了因果引导框架PEACE，该框架识别并利用仇恨内容中普遍存在的两个内在因果线索：文本的整体情感和攻击性。我们在多个存在分布偏移的平台上开展了大量实验，以验证因果线索是否有助于跨平台泛化。
英文摘要：
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics. Due to the different policies of the platforms, different groups of people express hate in different ways. Furthermore, due to the lack of labeled data in some platforms it becomes challenging to build hate speech detection models. To this end, we revisit if we can learn a generalizable hate speech detection model for the cross platform setting, where we train the model on the data from one (source) platform and generalize the model across multiple (target) platforms. Existing generalization models rely on linguistic cues or auxiliary information, making them biased towards certain tags or certain kinds of words (e.g., abusive words) on the source platform and thus not applicable to the target platforms. Inspired by social and psychological theories, we endeavor to explore if there exist inherent causal cues that can be leveraged to learn generalizable representations for detecting hate speech across these distribution shifts. To this end, we propose a causality-guided framework, PEACE, that identifies and leverages two intrinsic causal cues omnipresent in hateful content: the overall sentiment and the aggression in the text. We conduct extensive experiments across multiple platforms (representing the distribution shift) showing if causal cues can help cross-platform generalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
